Wenwei Zhang

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

A new microporous metal-organic framework Cu(2)(EBTC)(H(2)O)(2) x xG (EBTC = 1,1'-ethynebenzene-3,3',5,5'-tetracarboxylate; G = guest molecule) was rationally designed with a NbO net, exhibiting significantly high acetylene storage of 252 and 160 cm(3) g(-1) at 273 and 295 K under 1 bar, respectively.

Aiming to improve the acetylene (C2H2) storage capability of MOFs, we successfully designed NJU-Bai 17, a new analogue of MOF-505 with an inserted amide functional group which exhibits almost record high C2H2 uptakes of 222.4 cm(3) g(-1) at 296 K and 296 cm(3) g(-1) at 273 K under 1 bar. This result has been further supported by the determination of the heat of C2H2 adsorption and Grand Canonical Monte Carlo (GCMC) and first-principle calculations.

An ion-pair complex [FBzPy][Ni(mnt)2], where [FBzPy]+ = 1-(4'-fluorobenzyl)pyridinium and mnt2- = maleonitriledithiolate, forms a discrete stacking column and shows a peculiar magnetic transition from paramagnetic to diamagnetic around 90 K.